There Is No Preview Available For This Item
This item does not appear to have any files that can be experienced on Archive.org.
Please download files in this item to interact with them on your computer.
Show all files
TALK 1 - Structured Prediction Models in Computer Vision
Abstract: I'll present a summary of our recent work on using modern machine learning methods to solve computer vision problems. This essentially consists of using structured prediction models like max-margin structured estimators and conditional random fields. The computer vision problems we will discuss include graph matching, shape classification and object categorization.
TALK 2 - Efficient Convex Relaxation of Mixture Regression with Application to Motion Segmentation
Abstract: We give a semidefinite relaxation for maximum a posteriori estimation of a mixture of regression models. In addition we show how the semidefinite program can be exactly solved by a fast spectral method. We compare the proposed technique against Expectation-Maximization for synthetic problems as well as for problems of motion segmentation in computer vision, with promising results.